应用动态帧结构的宽带认知网络中信息和射频能量协同收集的研究

Compared with the traditional communication network, the cognitive network (CN) may consume more energy because of spectrum sensing, spectrum access and channel switch. Hence, how to improve the CN life has been a research hotspot. However, the scholars have paid wide attention to the method that CN harvests radio frequency energy to supply the electric power. In the current works, energy harvesting and information transmission are mutually independent, which ignores the internal relation of energy harvesting and spectrum access, and communication resources are allocated based on the instantaneous network state, which ignores the dynamically time-varying characteristics of spectrum access and energy propagation, hence, the energy harvesting efficiency is decreased. This project will build the spectrum state of primary network as an alternating Markov random process and accordingly construct the dynamic frame structure of broadband CN, in order to improve the probabilities of spectrum access and energy harvesting. Based on the frame structure, we will construct the spectrum access mode that improves energy harvesting efficiency through predicting energy propagation and accordingly investigate the cooperative harvesting models of energy and information. Through building the joint using scheme of the cooperative models for each subcarrier, we will improve both energy harvesting efficiency and information transmission efficiency. Based on the Markov decision process, we will predict the dynamic development of the CN states such as energy harvesting and spectrum access etc., and investigate the predictive dynamic joint resource management of time, subcarrier, power and energy, in order to improve the cooperative harvesting efficiency of information and energy under the long-term dynamic development of the CN. The research is intended to lay the groundwork for improving the working life and transmission profit of the spectrum-sharing network.

相对传统通信网络，认知网络因具有感知、接入和切换功能消耗更多能量，如何提高认知网络寿命已成为研究热点，而收集射频能量补充电能的方法受到学者广泛关注。现有的能量收集和信息传输相互独立，忽略了能量收集和频谱接入的内在联系，通信资源依据瞬时网络状态分配，忽略了频谱接入和能量传播的动态时变性，降低了能量收集效率。本项目将主网络频谱状态建模为交替马尔可夫过程，依此构建宽带认知网络动态帧结构，提高频谱接入和能量收集概率。基于帧结构，通过预测能量传播构建提高能量收集效率的频谱接入方式，依此研究信息和能量协同收集模型，通过建立各子载波模型的联合使用方案，提高能量收集和信息传输效率。基于马尔可夫决策过程预测能量收集和频谱接入等网络状态的动态发展，研究时间、载波、功率和能量等资源的预测性动态联合管理，提高认知网络长期动态发展下信息和能量协同收集的效率。通过研究拟为提高频谱共享网络的工作寿命和传输收益奠定基础。

认知无线电作为一种高效频谱利用系统，因具有感知、接入和切换功能会消耗巨大能量，如何节约认知无线电能耗和提高其工作寿命已成为研究热点。无线能量收集能够采集射频能量补充电能，认知无线电通过能量收集可以采集主用户和其它信号源的能量，从而节约系统能耗。现有能量收集和信息传输相互独立，忽略了信息传输和能量收集的协同性和资源分配的动态性。为解决上述问题，本项目提出应用动态帧结构的宽带认知无线电网络中信息和射频能量协同收集的研究。主要研究内容为：(1) 将主网络频谱状态建模为马尔可夫过程，依此构建宽带认知网络动态帧结构，通过最小化认知无线电的频谱接入损耗，获取最优的感知周期；(2) 研究认知无线电的信息和能量动态协同收集，考虑多载波认知无线电系统，子载波被分成2个集合，分别用于信息传输和能量传输，通过优化子载波动态分配策略和子载波功率，在保证传输速率的同时最大化能量采集性能；(3) 研究认知无线电协作通信场景下信息和能量收集的动态联合资源管理，考虑一个中继节点帮助源节点转发数据同时向中继接收端发送自身信息，该中继节点同时采集源节点的射频能量；通过优化第一和第二时隙的子载波分配集合、源节点的子载波分配功率和中继节点的子载波分配功率，在保证中继节点的能量和传输速率以及源节点和中继节点传输功率的基础上，最大化源节点和目的节点的传输速率。结果表明信息和能量协同收集能够在保障传输性能的同时有效节省认知无线电的传输能量，充分达到节省通信能耗的目的。基于该项目发表SCI检索论文35篇，ESI高被引论文3篇，授权专利2项。该项目为未来无线通信充分利用射频能量实现自供电，通过节约能耗实现绿色传输提供了技术保障。